Overview

Dataset statistics

Number of variables18
Number of observations999
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory180.6 KiB
Average record size in memory185.1 B

Variable types

Numeric15
Categorical3

Alerts

bathrooms is highly overall correlated with floors and 5 other fieldsHigh correlation
bedrooms is highly overall correlated with sqft_above and 1 other fieldsHigh correlation
floors is highly overall correlated with bathrooms and 1 other fieldsHigh correlation
grade is highly overall correlated with bathrooms and 4 other fieldsHigh correlation
lat is highly overall correlated with priceHigh correlation
long is highly overall correlated with zipcodeHigh correlation
price is highly overall correlated with grade and 4 other fieldsHigh correlation
sqft_above is highly overall correlated with bathrooms and 6 other fieldsHigh correlation
sqft_living is highly overall correlated with bathrooms and 5 other fieldsHigh correlation
sqft_living15 is highly overall correlated with bathrooms and 4 other fieldsHigh correlation
view is highly overall correlated with waterfrontHigh correlation
waterfront is highly overall correlated with viewHigh correlation
yr_built is highly overall correlated with bathroomsHigh correlation
zipcode is highly overall correlated with longHigh correlation
waterfront is highly imbalanced (93.3%)Imbalance
view is highly imbalanced (72.0%)Imbalance
sqft_basement has 598 (59.9%) zerosZeros
yr_renovated has 958 (95.9%) zerosZeros

Reproduction

Analysis started2025-12-23 04:22:54.166647
Analysis finished2025-12-23 04:23:50.604928
Duration56.44 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

bedrooms
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3493493
Minimum0
Maximum7
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size47.9 KiB
2025-12-23T09:53:50.732454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.85236737
Coefficient of variation (CV)0.25448745
Kurtosis0.83306099
Mean3.3493493
Median Absolute Deviation (MAD)1
Skewness0.35423566
Sum3346
Variance0.72653014
MonotonicityNot monotonic
2025-12-23T09:53:50.958183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3491
49.1%
4305
30.5%
2114
 
11.4%
569
 
6.9%
611
 
1.1%
17
 
0.7%
71
 
0.1%
01
 
0.1%
ValueCountFrequency (%)
01
 
0.1%
17
 
0.7%
2114
 
11.4%
3491
49.1%
4305
30.5%
569
 
6.9%
611
 
1.1%
71
 
0.1%
ValueCountFrequency (%)
71
 
0.1%
611
 
1.1%
569
 
6.9%
4305
30.5%
3491
49.1%
2114
 
11.4%
17
 
0.7%
01
 
0.1%

bathrooms
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0457958
Minimum0
Maximum5
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size47.9 KiB
2025-12-23T09:53:51.133112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.5
median2
Q32.5
95-th percentile3.25
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.72198328
Coefficient of variation (CV)0.35291073
Kurtosis0.39664366
Mean2.0457958
Median Absolute Deviation (MAD)0.5
Skewness0.30866374
Sum2043.75
Variance0.52125986
MonotonicityNot monotonic
2025-12-23T09:53:51.306013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2.5238
23.8%
1187
18.7%
1.75159
15.9%
2.25104
10.4%
298
9.8%
1.561
 
6.1%
2.7551
 
5.1%
333
 
3.3%
3.528
 
2.8%
3.2518
 
1.8%
Other values (9)22
 
2.2%
ValueCountFrequency (%)
01
 
0.1%
0.756
 
0.6%
1187
18.7%
1.251
 
0.1%
1.561
 
6.1%
1.75159
15.9%
298
9.8%
2.25104
10.4%
2.5238
23.8%
2.7551
 
5.1%
ValueCountFrequency (%)
52
 
0.2%
4.751
 
0.1%
4.52
 
0.2%
4.254
 
0.4%
44
 
0.4%
3.751
 
0.1%
3.528
2.8%
3.2518
 
1.8%
333
3.3%
2.7551
5.1%

sqft_living
Real number (ℝ)

High correlation 

Distinct321
Distinct (%)32.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2051.3974
Minimum380
Maximum6070
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.9 KiB
2025-12-23T09:53:51.535916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum380
5-th percentile990
Q11405
median1900
Q32475
95-th percentile3830
Maximum6070
Range5690
Interquartile range (IQR)1070

Descriptive statistics

Standard deviation888.35111
Coefficient of variation (CV)0.43304682
Kurtosis2.0253918
Mean2051.3974
Median Absolute Deviation (MAD)530
Skewness1.2472064
Sum2049346
Variance789167.7
MonotonicityNot monotonic
2025-12-23T09:53:51.836464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125011
 
1.1%
151011
 
1.1%
130010
 
1.0%
149010
 
1.0%
133010
 
1.0%
20209
 
0.9%
21609
 
0.9%
14309
 
0.9%
16709
 
0.9%
14009
 
0.9%
Other values (311)902
90.3%
ValueCountFrequency (%)
3801
 
0.1%
4301
 
0.1%
5601
 
0.1%
6302
0.2%
7001
 
0.1%
7201
 
0.1%
7401
 
0.1%
7502
0.2%
7601
 
0.1%
7703
0.3%
ValueCountFrequency (%)
60701
 
0.1%
60502
0.2%
54201
 
0.1%
54031
 
0.1%
53101
 
0.1%
51801
 
0.1%
50501
 
0.1%
48901
 
0.1%
48701
 
0.1%
48603
0.3%

sqft_lot
Real number (ℝ)

Distinct828
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14707.242
Minimum649
Maximum315374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.9 KiB
2025-12-23T09:53:52.165745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum649
5-th percentile2871.5
Q15419
median8040
Q311508.5
95-th percentile40445.2
Maximum315374
Range314725
Interquartile range (IQR)6089.5

Descriptive statistics

Standard deviation28975.077
Coefficient of variation (CV)1.9701231
Kurtosis41.996043
Mean14707.242
Median Absolute Deviation (MAD)2920
Skewness6.0808509
Sum14692535
Variance8.395551 × 108
MonotonicityNot monotonic
2025-12-23T09:53:52.449442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500027
 
2.7%
600012
 
1.2%
400010
 
1.0%
96007
 
0.7%
90006
 
0.6%
30005
 
0.5%
75005
 
0.5%
72005
 
0.5%
54005
 
0.5%
84005
 
0.5%
Other values (818)912
91.3%
ValueCountFrequency (%)
6491
0.1%
10161
0.1%
10441
0.1%
10581
0.1%
10661
0.1%
10861
0.1%
10911
0.1%
11001
0.1%
11021
0.1%
11401
0.1%
ValueCountFrequency (%)
3153741
0.1%
2620181
0.1%
2306521
0.1%
2212841
0.1%
2199781
0.1%
2182521
0.1%
2178001
0.1%
2170141
0.1%
2134441
0.1%
2099591
0.1%

floors
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4469469
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.9 KiB
2025-12-23T09:53:52.647074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.51742017
Coefficient of variation (CV)0.35759443
Kurtosis-0.43785566
Mean1.4469469
Median Absolute Deviation (MAD)0
Skewness0.68618829
Sum1445.5
Variance0.26772364
MonotonicityNot monotonic
2025-12-23T09:53:52.806799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1528
52.9%
2354
35.4%
1.593
 
9.3%
318
 
1.8%
2.55
 
0.5%
3.51
 
0.1%
ValueCountFrequency (%)
1528
52.9%
1.593
 
9.3%
2354
35.4%
2.55
 
0.5%
318
 
1.8%
3.51
 
0.1%
ValueCountFrequency (%)
3.51
 
0.1%
318
 
1.8%
2.55
 
0.5%
2354
35.4%
1.593
 
9.3%
1528
52.9%

waterfront
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size47.9 KiB
0.0
991 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2997
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0991
99.2%
1.08
 
0.8%

Length

2025-12-23T09:53:53.043277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-23T09:53:53.164982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0991
99.2%
1.08
 
0.8%

Most occurring characters

ValueCountFrequency (%)
01990
66.4%
.999
33.3%
18
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)2997
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01990
66.4%
.999
33.3%
18
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2997
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01990
66.4%
.999
33.3%
18
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2997
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01990
66.4%
.999
33.3%
18
 
0.3%

view
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size47.9 KiB
0.0
899 
2.0
 
46
3.0
 
26
1.0
 
15
4.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2997
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0899
90.0%
2.046
 
4.6%
3.026
 
2.6%
1.015
 
1.5%
4.013
 
1.3%

Length

2025-12-23T09:53:53.695972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-23T09:53:53.848888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0899
90.0%
2.046
 
4.6%
3.026
 
2.6%
1.015
 
1.5%
4.013
 
1.3%

Most occurring characters

ValueCountFrequency (%)
01898
63.3%
.999
33.3%
246
 
1.5%
326
 
0.9%
115
 
0.5%
413
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)2997
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01898
63.3%
.999
33.3%
246
 
1.5%
326
 
0.9%
115
 
0.5%
413
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2997
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01898
63.3%
.999
33.3%
246
 
1.5%
326
 
0.9%
115
 
0.5%
413
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2997
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01898
63.3%
.999
33.3%
246
 
1.5%
326
 
0.9%
115
 
0.5%
413
 
0.4%

condition
Categorical

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size47.9 KiB
3.0
612 
4.0
280 
5.0
98 
2.0
 
6
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2997
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row5.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0612
61.3%
4.0280
28.0%
5.098
 
9.8%
2.06
 
0.6%
1.03
 
0.3%

Length

2025-12-23T09:53:54.040907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-23T09:53:54.186170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.0612
61.3%
4.0280
28.0%
5.098
 
9.8%
2.06
 
0.6%
1.03
 
0.3%

Most occurring characters

ValueCountFrequency (%)
.999
33.3%
0999
33.3%
3612
20.4%
4280
 
9.3%
598
 
3.3%
26
 
0.2%
13
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2997
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.999
33.3%
0999
33.3%
3612
20.4%
4280
 
9.3%
598
 
3.3%
26
 
0.2%
13
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2997
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.999
33.3%
0999
33.3%
3612
20.4%
4280
 
9.3%
598
 
3.3%
26
 
0.2%
13
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2997
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.999
33.3%
0999
33.3%
3612
20.4%
4280
 
9.3%
598
 
3.3%
26
 
0.2%
13
 
0.1%

grade
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6056056
Minimum4
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.9 KiB
2025-12-23T09:53:54.354201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6
Q17
median7
Q38
95-th percentile10
Maximum12
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1607339
Coefficient of variation (CV)0.15261558
Kurtosis1.3395351
Mean7.6056056
Median Absolute Deviation (MAD)1
Skewness0.83089173
Sum7598
Variance1.3473032
MonotonicityNot monotonic
2025-12-23T09:53:54.531384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
7445
44.5%
8266
26.6%
9112
 
11.2%
691
 
9.1%
1047
 
4.7%
1118
 
1.8%
513
 
1.3%
125
 
0.5%
42
 
0.2%
ValueCountFrequency (%)
42
 
0.2%
513
 
1.3%
691
 
9.1%
7445
44.5%
8266
26.6%
9112
 
11.2%
1047
 
4.7%
1118
 
1.8%
125
 
0.5%
ValueCountFrequency (%)
125
 
0.5%
1118
 
1.8%
1047
 
4.7%
9112
 
11.2%
8266
26.6%
7445
44.5%
691
 
9.1%
513
 
1.3%
42
 
0.2%

sqft_above
Real number (ℝ)

High correlation 

Distinct291
Distinct (%)29.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1750.2332
Minimum380
Maximum6070
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.9 KiB
2025-12-23T09:53:54.740364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum380
5-th percentile869
Q11190
median1540
Q32135
95-th percentile3300
Maximum6070
Range5690
Interquartile range (IQR)945

Descriptive statistics

Standard deviation790.46691
Coefficient of variation (CV)0.45163518
Kurtosis3.1296995
Mean1750.2332
Median Absolute Deviation (MAD)430
Skewness1.4790359
Sum1748483
Variance624837.93
MonotonicityNot monotonic
2025-12-23T09:53:54.968202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125014
 
1.4%
101014
 
1.4%
130014
 
1.4%
133012
 
1.2%
161011
 
1.1%
132011
 
1.1%
110011
 
1.1%
100010
 
1.0%
107010
 
1.0%
113010
 
1.0%
Other values (281)882
88.3%
ValueCountFrequency (%)
3801
 
0.1%
4301
 
0.1%
5601
 
0.1%
5801
 
0.1%
6302
0.2%
6701
 
0.1%
7004
0.4%
7202
0.2%
7401
 
0.1%
7502
0.2%
ValueCountFrequency (%)
60701
0.1%
60501
0.1%
54031
0.1%
53101
0.1%
48601
0.1%
47501
0.1%
47401
0.1%
46701
0.1%
45701
0.1%
44101
0.1%

sqft_basement
Real number (ℝ)

Zeros 

Distinct140
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean301.16416
Minimum0
Maximum2060
Zeros598
Zeros (%)59.9%
Negative0
Negative (%)0.0%
Memory size47.9 KiB
2025-12-23T09:53:55.283524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3580
95-th percentile1223
Maximum2060
Range2060
Interquartile range (IQR)580

Descriptive statistics

Standard deviation451.0234
Coefficient of variation (CV)1.4975998
Kurtosis1.4506011
Mean301.16416
Median Absolute Deviation (MAD)0
Skewness1.4714516
Sum300863
Variance203422.11
MonotonicityNot monotonic
2025-12-23T09:53:55.622383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0598
59.9%
60014
 
1.4%
50013
 
1.3%
70013
 
1.3%
40012
 
1.2%
80010
 
1.0%
3008
 
0.8%
10407
 
0.7%
5307
 
0.7%
10107
 
0.7%
Other values (130)310
31.0%
ValueCountFrequency (%)
0598
59.9%
502
 
0.2%
601
 
0.1%
1203
 
0.3%
1302
 
0.2%
1403
 
0.3%
1501
 
0.1%
1603
 
0.3%
1803
 
0.3%
1901
 
0.1%
ValueCountFrequency (%)
20601
0.1%
20001
0.1%
19502
0.2%
19001
0.1%
18301
0.1%
18201
0.1%
18101
0.1%
18001
0.1%
17801
0.1%
17602
0.2%

yr_built
Real number (ℝ)

High correlation 

Distinct114
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1969.03
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.9 KiB
2025-12-23T09:53:56.002143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1915
Q11952
median1974
Q31992
95-th percentile2006
Maximum2015
Range115
Interquartile range (IQR)40

Descriptive statistics

Standard deviation28.198607
Coefficient of variation (CV)0.014321065
Kurtosis-0.53885541
Mean1969.03
Median Absolute Deviation (MAD)20
Skewness-0.54476677
Sum1967061
Variance795.16142
MonotonicityNot monotonic
2025-12-23T09:53:56.354582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
195425
 
2.5%
200525
 
2.5%
197720
 
2.0%
197920
 
2.0%
199419
 
1.9%
196819
 
1.9%
197819
 
1.9%
200318
 
1.8%
200418
 
1.8%
198717
 
1.7%
Other values (104)799
80.0%
ValueCountFrequency (%)
19006
0.6%
19012
 
0.2%
19021
 
0.1%
19031
 
0.1%
19043
0.3%
19055
0.5%
19072
 
0.2%
19085
0.5%
19094
0.4%
19105
0.5%
ValueCountFrequency (%)
20151
 
0.1%
201410
1.0%
20133
 
0.3%
20123
 
0.3%
20111
 
0.1%
20106
0.6%
20094
 
0.4%
20088
0.8%
20079
0.9%
200613
1.3%

yr_renovated
Real number (ℝ)

Zeros 

Distinct25
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.830831
Minimum0
Maximum2014
Zeros958
Zeros (%)95.9%
Negative0
Negative (%)0.0%
Memory size47.9 KiB
2025-12-23T09:53:56.709497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2014
Range2014
Interquartile range (IQR)0

Descriptive statistics

Standard deviation395.76792
Coefficient of variation (CV)4.8364157
Kurtosis19.518878
Mean81.830831
Median Absolute Deviation (MAD)0
Skewness4.6344404
Sum81749
Variance156632.24
MonotonicityNot monotonic
2025-12-23T09:53:57.060313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0958
95.9%
19913
 
0.3%
20023
 
0.3%
20133
 
0.3%
20053
 
0.3%
20033
 
0.3%
20143
 
0.3%
19782
 
0.2%
19992
 
0.2%
19902
 
0.2%
Other values (15)17
 
1.7%
ValueCountFrequency (%)
0958
95.9%
19451
 
0.1%
19541
 
0.1%
19571
 
0.1%
19741
 
0.1%
19771
 
0.1%
19782
 
0.2%
19811
 
0.1%
19831
 
0.1%
19842
 
0.2%
ValueCountFrequency (%)
20143
0.3%
20133
0.3%
20111
 
0.1%
20101
 
0.1%
20081
 
0.1%
20053
0.3%
20033
0.3%
20023
0.3%
19992
0.2%
19951
 
0.1%

zipcode
Real number (ℝ)

High correlation 

Distinct69
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98074.441
Minimum98001
Maximum98199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.9 KiB
2025-12-23T09:53:57.510695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum98001
5-th percentile98004
Q198032
median98058
Q398116
95-th percentile98177
Maximum98199
Range198
Interquartile range (IQR)84

Descriptive statistics

Standard deviation52.545832
Coefficient of variation (CV)0.00053577498
Kurtosis-0.73273415
Mean98074.441
Median Absolute Deviation (MAD)44
Skewness0.51323728
Sum97976367
Variance2761.0645
MonotonicityNot monotonic
2025-12-23T09:53:57.887297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9803838
 
3.8%
9800632
 
3.2%
9802330
 
3.0%
9805229
 
2.9%
9805827
 
2.7%
9813327
 
2.7%
9804227
 
2.7%
9810327
 
2.7%
9803426
 
2.6%
9811526
 
2.6%
Other values (59)710
71.1%
ValueCountFrequency (%)
9800116
1.6%
980028
 
0.8%
9800316
1.6%
9800413
1.3%
980058
 
0.8%
9800632
3.2%
980074
 
0.4%
980089
 
0.9%
980107
 
0.7%
9801111
 
1.1%
ValueCountFrequency (%)
981997
0.7%
9819814
1.4%
981887
0.7%
9817816
1.6%
9817710
1.0%
9816813
1.3%
9816616
1.6%
981559
0.9%
981482
 
0.2%
9814611
1.1%

lat
Real number (ℝ)

High correlation 

Distinct895
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.54972
Minimum47.1775
Maximum47.7776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.9 KiB
2025-12-23T09:53:58.270748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum47.1775
5-th percentile47.31017
Q147.443
median47.5636
Q347.6734
95-th percentile47.74792
Maximum47.7776
Range0.6001
Interquartile range (IQR)0.2304

Descriptive statistics

Standard deviation0.14155835
Coefficient of variation (CV)0.0029770595
Kurtosis-0.87846204
Mean47.54972
Median Absolute Deviation (MAD)0.1141
Skewness-0.35819307
Sum47502.17
Variance0.020038766
MonotonicityNot monotonic
2025-12-23T09:53:58.597505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.70734
 
0.4%
47.36083
 
0.3%
47.67343
 
0.3%
47.65973
 
0.3%
47.36633
 
0.3%
47.71453
 
0.3%
47.48023
 
0.3%
47.51232
 
0.2%
47.38282
 
0.2%
47.692
 
0.2%
Other values (885)971
97.2%
ValueCountFrequency (%)
47.17751
0.1%
47.18031
0.1%
47.19131
0.1%
47.19491
0.1%
47.19511
0.1%
47.19761
0.1%
47.20861
0.1%
47.21051
0.1%
47.21181
0.1%
47.24131
0.1%
ValueCountFrequency (%)
47.77761
0.1%
47.77671
0.1%
47.77511
0.1%
47.77381
0.1%
47.77362
0.2%
47.77341
0.1%
47.77311
0.1%
47.77281
0.1%
47.77271
0.1%
47.77211
0.1%

long
Real number (ℝ)

High correlation 

Distinct405
Distinct (%)40.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.20741
Minimum-122.49
Maximum-121.709
Zeros0
Zeros (%)0.0%
Negative999
Negative (%)100.0%
Memory size47.9 KiB
2025-12-23T09:53:58.937073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-122.49
5-th percentile-122.382
Q1-122.3225
median-122.218
Q3-122.118
95-th percentile-121.9738
Maximum-121.709
Range0.781
Interquartile range (IQR)0.2045

Descriptive statistics

Standard deviation0.13956378
Coefficient of variation (CV)-0.0011420239
Kurtosis0.21344681
Mean-122.20741
Median Absolute Deviation (MAD)0.102
Skewness0.73404715
Sum-122085.2
Variance0.019478049
MonotonicityNot monotonic
2025-12-23T09:53:59.345716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.1889
 
0.9%
-122.1918
 
0.8%
-122.1987
 
0.7%
-122.3717
 
0.7%
-122.3727
 
0.7%
-122.1257
 
0.7%
-122.3197
 
0.7%
-122.3817
 
0.7%
-122.3537
 
0.7%
-122.2867
 
0.7%
Other values (395)926
92.7%
ValueCountFrequency (%)
-122.491
 
0.1%
-122.4821
 
0.1%
-122.4511
 
0.1%
-122.4382
0.2%
-122.4111
 
0.1%
-122.4091
 
0.1%
-122.4051
 
0.1%
-122.4023
0.3%
-122.42
0.2%
-122.3963
0.3%
ValueCountFrequency (%)
-121.7091
0.1%
-121.7111
0.1%
-121.7141
0.1%
-121.7551
0.1%
-121.7581
0.1%
-121.7591
0.1%
-121.7711
0.1%
-121.7721
0.1%
-121.7761
0.1%
-121.7791
0.1%

sqft_living15
Real number (ℝ)

High correlation 

Distinct267
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1986.8138
Minimum830
Maximum4760
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.9 KiB
2025-12-23T09:53:59.698092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum830
5-th percentile1169
Q11490
median1850
Q32360
95-th percentile3251
Maximum4760
Range3930
Interquartile range (IQR)870

Descriptive statistics

Standard deviation670.72347
Coefficient of variation (CV)0.33758748
Kurtosis1.0944439
Mean1986.8138
Median Absolute Deviation (MAD)410
Skewness1.049913
Sum1984827
Variance449869.98
MonotonicityNot monotonic
2025-12-23T09:54:00.149273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157012
 
1.2%
158011
 
1.1%
132011
 
1.1%
156011
 
1.1%
139010
 
1.0%
146010
 
1.0%
159010
 
1.0%
144010
 
1.0%
161010
 
1.0%
166010
 
1.0%
Other values (257)894
89.5%
ValueCountFrequency (%)
8301
 
0.1%
8801
 
0.1%
8901
 
0.1%
9401
 
0.1%
9502
0.2%
9701
 
0.1%
9801
 
0.1%
10002
0.2%
10103
0.3%
10204
0.4%
ValueCountFrequency (%)
47601
0.1%
46801
0.1%
45501
0.1%
43001
0.1%
42301
0.1%
42101
0.1%
41901
0.1%
41801
0.1%
41101
0.1%
41001
0.1%

price
Real number (ℝ)

High correlation 

Distinct580
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.071452
Minimum8
Maximum308
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.9 KiB
2025-12-23T09:54:00.604028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile21
Q130.98
median43.5
Q363.44625
95-th percentile110
Maximum308
Range300
Interquartile range (IQR)32.46625

Descriptive statistics

Standard deviation33.974907
Coefficient of variation (CV)0.65246705
Kurtosis13.948624
Mean52.071452
Median Absolute Deviation (MAD)14.6651
Skewness2.9749338
Sum52019.38
Variance1154.2943
MonotonicityNot monotonic
2025-12-23T09:54:00.965744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6513
 
1.3%
31.510
 
1.0%
269
 
0.9%
42.59
 
0.9%
288
 
0.8%
538
 
0.8%
328
 
0.8%
338
 
0.8%
38.57
 
0.7%
457
 
0.7%
Other values (570)912
91.3%
ValueCountFrequency (%)
81
0.1%
131
0.1%
14.751
0.1%
15.31
0.1%
15.71
0.1%
161
0.1%
16.351
0.1%
16.51
0.1%
16.661
0.1%
16.6951
0.1%
ValueCountFrequency (%)
3081
 
0.1%
3071
 
0.1%
2901
 
0.1%
2402
0.2%
2381
 
0.1%
2253
0.3%
2131
 
0.1%
2051
 
0.1%
2001
 
0.1%
1951
 
0.1%

Interactions

2025-12-23T09:53:46.173452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:52:55.341954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:52:59.231550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:02.677418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:06.003587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:09.434241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:12.838276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:16.208884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:19.659080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:23.080645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:26.853597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:30.485512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:34.320638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:38.002831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:42.234245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:46.462336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:52:55.541158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:52:59.457678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:02.896106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:06.250121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:09.668876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:13.051637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:16.454759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:19.871815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:23.310755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:27.111930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:30.714609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:34.538324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:38.251689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:42.525039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:46.681749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:52:55.761318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:52:59.673460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:03.105755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:06.447766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:09.894755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:13.259984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:16.664169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:20.081833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:23.507281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:27.347040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:30.972252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:34.779611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:38.526697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:42.756774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:46.906753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:52:55.998091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:52:59.875929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:03.335977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:06.671361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:10.136670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:13.489311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:16.857206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:20.287970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:23.775438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:27.601444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:31.212523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:35.001426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:38.766895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:43.029064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:47.163821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:52:56.221219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:00.119911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:03.539463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:06.935946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:10.357887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:13.717668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:17.070513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:20.512772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:23.990973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:27.849402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:31.443489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:35.221675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:39.001978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:43.298721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:47.389740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:52:56.486005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:00.342889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:03.739885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:07.133622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:10.589328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:13.928948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:17.305231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:20.758159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:24.195164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:28.062478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:31.688164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:35.475716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:39.261917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:43.617292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:47.651779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:52:56.994688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:00.544035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:04.006293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:07.364823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:10.788738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:14.139054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:17.527982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:20.982260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:24.442899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:28.322442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:31.965649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:35.711654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:39.495283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:43.872908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:47.969435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:52:57.219724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:00.755792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:04.203921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:07.640140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:11.000366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:14.378810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:17.749246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:21.194015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:24.659835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:28.532060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:32.214089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:35.932418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:39.725837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:44.103267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:48.182904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:52:57.459605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:01.008569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:04.404540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:07.835868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:11.236973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:14.583390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:17.971602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:21.404575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:24.879810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:28.727833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:32.474704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:36.160488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:40.079929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:44.350605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:48.400025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:52:57.709573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:01.223039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:04.655057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:08.037498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:11.471169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:14.790259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:18.175885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:21.643573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:25.099782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:28.986994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:32.743746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:36.390235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:40.404735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:44.578158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:48.637724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:52:57.950482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:01.444357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:04.866195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:08.302538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:11.679625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:15.074139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:18.395326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:21.862976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:25.647798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:29.208502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:32.981667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:36.632471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:40.795056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:44.838423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:48.874924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:52:58.231978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:01.686285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:05.094985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:08.538714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:11.887786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:15.303086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:18.660801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:22.073130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:25.921820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:29.457861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:33.303523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:36.904358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:41.117509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:45.134816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:49.104840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:52:58.470669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:01.886834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:05.359639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:08.747462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:12.111829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:15.505960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:18.865598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:22.341096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:26.134192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:29.736641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:33.554896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:37.141528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:41.391881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:45.375284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:49.402781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:52:58.755753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:02.113215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:05.588475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:09.001113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:12.329281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:15.778230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:19.181442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:22.577935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:26.358848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:29.998554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:33.806362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:37.379656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:41.669553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:45.655541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:49.639915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:52:58.981484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:02.375976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:05.790778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:09.229189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:12.563473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:15.997240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:19.445839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:22.816005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:26.593059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:30.235396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:34.093735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:37.743360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:41.944479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-23T09:53:45.927373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-23T09:54:01.278610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
bathroomsbedroomsconditionfloorsgradelatlongpricesqft_abovesqft_basementsqft_livingsqft_living15sqft_lotviewwaterfrontyr_builtyr_renovatedzipcode
bathrooms1.0000.4820.1090.5190.6550.0250.3040.4700.6910.1770.7320.6120.1150.1350.1830.5700.026-0.236
bedrooms0.4821.0000.0710.2370.372-0.0070.1710.3290.5270.1850.6120.4330.2020.1540.0990.1510.014-0.174
condition0.1090.0711.0000.1620.1630.0670.0950.0000.0650.0780.0080.0490.0790.0000.0000.2420.0670.096
floors0.5190.2370.1621.0000.4620.0650.1750.3280.618-0.3070.4030.323-0.1680.0540.0000.4640.046-0.027
grade0.6550.3720.1630.4621.0000.1280.2320.6260.6950.1330.7210.6820.1710.1470.1800.484-0.004-0.185
lat0.025-0.0070.0670.0650.1281.000-0.1150.5510.0100.1600.1010.083-0.1340.0560.078-0.1420.0570.259
long0.3040.1710.0950.1750.232-0.1151.0000.0490.388-0.2050.2730.3750.3930.0690.4660.485-0.095-0.516
price0.4700.3290.0000.3280.6260.5510.0491.0000.5150.2760.6320.5780.0600.3290.4610.0580.1000.012
sqft_above0.6910.5270.0650.6180.6950.0100.3880.5151.000-0.1610.8340.7170.2900.1180.2410.4550.029-0.284
sqft_basement0.1770.1850.078-0.3070.1330.160-0.2050.276-0.1611.0000.3500.1760.0500.2180.182-0.1770.0750.060
sqft_living0.7320.6120.0080.4030.7210.1010.2730.6320.8340.3501.0000.7890.3140.1860.1670.3350.035-0.229
sqft_living150.6120.4330.0490.3230.6820.0830.3750.5780.7170.1760.7891.0000.3740.1980.2180.356-0.016-0.295
sqft_lot0.1150.2020.079-0.1680.171-0.1340.3930.0600.2900.0500.3140.3741.0000.0250.0450.072-0.002-0.346
view0.1350.1540.0000.0540.1470.0560.0690.3290.1180.2180.1860.1980.0251.0000.5870.0000.0850.000
waterfront0.1830.0990.0000.0000.1800.0780.4660.4610.2410.1820.1670.2180.0450.5871.0000.0000.0000.176
yr_built0.5700.1510.2420.4640.484-0.1420.4850.0580.455-0.1770.3350.3560.0720.0000.0001.000-0.243-0.353
yr_renovated0.0260.0140.0670.046-0.0040.057-0.0950.1000.0290.0750.035-0.016-0.0020.0850.000-0.2431.0000.089
zipcode-0.236-0.1740.096-0.027-0.1850.259-0.5160.012-0.2840.060-0.229-0.295-0.3460.0000.176-0.3530.0891.000

Missing values

2025-12-23T09:53:50.033610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-23T09:53:50.421599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

bedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15price
03.01.001180.05650.01.00.00.03.07.01180.00.01955.00.098178.047.5112-122.2571340.022.190
13.02.252570.07242.02.00.00.03.07.02170.0400.01951.01991.098125.047.7210-122.3191690.053.800
22.01.00770.010000.01.00.00.03.06.0770.00.01933.00.098028.047.7379-122.2332720.018.000
34.03.001960.05000.01.00.00.05.07.01050.0910.01965.00.098136.047.5208-122.3931360.060.400
43.02.001680.08080.01.00.00.03.08.01680.00.01987.00.098074.047.6168-122.0451800.051.000
54.04.505420.0101930.01.00.00.03.011.03890.01530.02001.00.098053.047.6561-122.0054760.0123.000
63.02.251715.06819.02.00.00.03.07.01715.00.01995.00.098003.047.3097-122.3272238.025.750
73.01.501060.09711.01.00.00.03.07.01060.00.01963.00.098198.047.4095-122.3151650.029.185
83.01.001780.07470.01.00.00.03.07.01050.0730.01960.00.098146.047.5123-122.3371780.022.950
93.02.501890.06560.02.00.00.03.07.01890.00.02003.00.098038.047.3684-122.0312390.032.300
bedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15price
9893.02.251670.05004.02.00.00.03.08.01670.00.01987.00.098029.047.5688-122.0171850.048.495
9904.01.752060.09828.01.00.00.04.08.02060.00.01960.00.098005.047.5867-122.1742260.064.000
9914.02.502160.08158.01.00.00.04.08.01660.0500.01952.00.098115.047.6948-122.3281520.058.500
9924.02.002780.011583.01.00.03.03.08.01190.01590.01955.00.098125.047.7293-122.2842580.064.500
9933.02.001490.07651.01.00.00.03.07.01490.00.01988.00.098003.047.3211-122.3251590.025.300
9942.01.00740.06460.01.00.00.03.06.0740.00.01953.00.098146.047.5077-122.3441170.017.850
9954.02.501860.06325.02.00.00.04.07.01860.00.01991.00.098038.047.3492-122.0301860.029.100
9962.02.751590.020917.01.50.00.03.05.01590.00.01920.00.098001.047.2786-122.2501310.019.995
9972.01.00850.02340.01.00.00.03.07.0850.00.01922.00.098105.047.6707-122.3281300.055.350
9982.01.001030.04188.01.00.00.03.08.01030.00.01981.00.098038.047.3738-122.0571450.018.995